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Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes.
Accid Anal Prev. 2011 Jan; 43(1):112-22.AA

Abstract

A multivariate logistic regression model, based upon National Automotive Sampling System Crashworthiness Data System (NASS-CDS) data for calendar years 1999-2008, was developed to predict the probability that a crash-involved vehicle will contain one or more occupants with serious or incapacitating injuries. These vehicles were defined as containing at least one occupant coded with an Injury Severity Score (ISS) of greater than or equal to 15, in planar, non-rollover crash events involving Model Year 2000 and newer cars, light trucks, and vans. The target injury outcome measure was developed by the Centers for Disease Control and Prevention (CDC)-led National Expert Panel on Field Triage in their recent revision of the Field Triage Decision Scheme (American College of Surgeons, 2006). The parameters to be used for crash injury prediction were subsequently specified by the National Expert Panel. Model input parameters included: crash direction (front, left, right, and rear), change in velocity (delta-V), multiple vs. single impacts, belt use, presence of at least one older occupant (≥ 55 years old), presence of at least one female in the vehicle, and vehicle type (car, pickup truck, van, and sport utility). The model was developed using predictor variables that may be readily available, post-crash, from OnStar-like telematics systems. Model sensitivity and specificity were 40% and 98%, respectively, using a probability cutpoint of 0.20. The area under the receiver operator characteristic (ROC) curve for the final model was 0.84. Delta-V (mph), seat belt use and crash direction were the most important predictors of serious injury. Due to the complexity of factors associated with rollover-related injuries, a separate screening algorithm is needed to model injuries associated with this crash mode.

Authors+Show Affiliations

General Motors Corporation, Vehicle Structure and Safety Integration, 30200 Mound Road, Warren, MI 48090, United States. douglas.w.kononen@gm.comNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Validation Study

Language

eng

PubMed ID

21094304

Citation

Kononen, Douglas W., et al. "Identification and Validation of a Logistic Regression Model for Predicting Serious Injuries Associated With Motor Vehicle Crashes." Accident; Analysis and Prevention, vol. 43, no. 1, 2011, pp. 112-22.
Kononen DW, Flannagan CA, Wang SC. Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes. Accid Anal Prev. 2011;43(1):112-22.
Kononen, D. W., Flannagan, C. A., & Wang, S. C. (2011). Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes. Accident; Analysis and Prevention, 43(1), 112-22. https://doi.org/10.1016/j.aap.2010.07.018
Kononen DW, Flannagan CA, Wang SC. Identification and Validation of a Logistic Regression Model for Predicting Serious Injuries Associated With Motor Vehicle Crashes. Accid Anal Prev. 2011;43(1):112-22. PubMed PMID: 21094304.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes. AU - Kononen,Douglas W, AU - Flannagan,Carol A C, AU - Wang,Stewart C, Y1 - 2010/08/21/ PY - 2010/04/01/received PY - 2010/07/26/revised PY - 2010/07/29/accepted PY - 2010/11/25/entrez PY - 2010/11/26/pubmed PY - 2011/3/30/medline SP - 112 EP - 22 JF - Accident; analysis and prevention JO - Accid Anal Prev VL - 43 IS - 1 N2 - A multivariate logistic regression model, based upon National Automotive Sampling System Crashworthiness Data System (NASS-CDS) data for calendar years 1999-2008, was developed to predict the probability that a crash-involved vehicle will contain one or more occupants with serious or incapacitating injuries. These vehicles were defined as containing at least one occupant coded with an Injury Severity Score (ISS) of greater than or equal to 15, in planar, non-rollover crash events involving Model Year 2000 and newer cars, light trucks, and vans. The target injury outcome measure was developed by the Centers for Disease Control and Prevention (CDC)-led National Expert Panel on Field Triage in their recent revision of the Field Triage Decision Scheme (American College of Surgeons, 2006). The parameters to be used for crash injury prediction were subsequently specified by the National Expert Panel. Model input parameters included: crash direction (front, left, right, and rear), change in velocity (delta-V), multiple vs. single impacts, belt use, presence of at least one older occupant (≥ 55 years old), presence of at least one female in the vehicle, and vehicle type (car, pickup truck, van, and sport utility). The model was developed using predictor variables that may be readily available, post-crash, from OnStar-like telematics systems. Model sensitivity and specificity were 40% and 98%, respectively, using a probability cutpoint of 0.20. The area under the receiver operator characteristic (ROC) curve for the final model was 0.84. Delta-V (mph), seat belt use and crash direction were the most important predictors of serious injury. Due to the complexity of factors associated with rollover-related injuries, a separate screening algorithm is needed to model injuries associated with this crash mode. SN - 1879-2057 UR - https://www.unboundmedicine.com/medline/citation/21094304/Identification_and_validation_of_a_logistic_regression_model_for_predicting_serious_injuries_associated_with_motor_vehicle_crashes_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0001-4575(10)00206-X DB - PRIME DP - Unbound Medicine ER -